# Plasmon Resonance Model: Investigation of Analysis of Fake News Diffusion Model with Third Mover Intervention Using Soliton Solution in Non-Complete Information Game under Repeated Dilemma Condition

arXiv: https://arxiv.org/abs/2403.05585 · HTML: https://mlsanity.com/paper/2403.05585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 129 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship](https://mlsanity.com/paper/1801.01665/) (2018)
- [Opinion dynamics: models, extensions and external effects](https://mlsanity.com/paper/1605.06326/) (2016)
- [Opinion Dynamics Theory for Analysis of Consensus Formation and Division of Opinion on the Internet](https://mlsanity.com/paper/1812.11845/) (2018)

## Abstract (arXiv)

In this research note, we propose a new approach to model the fake news diffusion process within the framework of incomplete information games. In particular, we use nonlinear partial differential equations to represent the phenomenon of plasmon resonance, in which the diffusion of fake news is rapidly amplified within a particular social group or communication network, and analyze its dynamics through a soliton solution approach. In addition, we consider how first mover, second mover, and third mover strategies interact within this nonlinear system and contribute to the amplification or suppression of fake news diffusion. The model aims to understand the mechanisms of fake news proliferation and provide insights into how to prevent or combat it. By combining concepts from the social sciences and the physical sciences, this study attempts to develop a new theoretical framework for the contemporary problem of fake news.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.